AI agents call delete_asset to permanently remove resources in Uefn — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Deleting an asset cannot be undone and permanently removes project content. This is a destructive operation that could cause significant loss of work. While the impact is scoped to the editor project rather than production systems, the irreversibility and potential for agent misuse (e.g., deleting critical assets by mistake) justifies 'high' severity.
From the tool's definition Tool name 'delete_asset' combined with description 'Delete an asset.' indicates irreversible deletion of data within the UEFN editor project.
Documented attack patterns abuse exactly the kind of access delete_asset gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Uefn, and nothing reaches the server without passing your rules. This is the rule we recommend for delete_asset:
{
"version": "1",
"default": "deny",
"hide": [
"delete_asset"
]
} delete_asset disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
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Delete an asset. It is categorised as a Destructive tool in the Uefn MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Uefn MCP server in PolicyLayer and add a rule for delete_asset: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Uefn. Nothing to install.
delete_asset is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the delete_asset rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for delete_asset. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
delete_asset is provided by the Uefn MCP server (quangdang46/uefn-verse-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Uefn, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
143 Uefn tools catalogued and risk-classified — across an index of 43,000+ MCP servers.